Researchers at Princeton University (New Jersey, USA) have just developed a new three-dimensional computing device that combines living brain cells with advanced electronic systems, marking a remarkable step forward in the field of bioinformatics.
According to research published in Nature Electronics (a specialized scientific journal under the prestigious publishing group Nature Portfolio), this device can be programmed to recognize and process simple patterns by using a network of active biological nerve cells with micro-electrodes.
Unlike previous studies that used nerve cells cultured on flat surfaces or clusters of cells that could only be tracked from the outside, the Princeton research group has built an electronic system located directly inside the living nerve network.
To do this, scientists created a three-dimensional support frame consisting of metal wires and micro-electrodes, covered with a layer of soft material compatible with biological tissue.
This structure allows tens of thousands of nerve cells to grow around the electronic network, forming an integrated three-dimensional bionic nervous system.
The device currently contains about 70,000 living nerve cells connected to dozens of micro-electrons. The system not only records electrical signals from nerve cells but can also stimulate them to function in real time.
The research team said the device demonstrated the ability to recognize relatively simple signal patterns in a controlled environment.
Although still in its early stages, this technology is expected to develop into a more complex bioinformation processing platform in the future.
According to the development team, the most important point of the project is the ability to directly integrate between living nerve cells and electronic hardware in the same operating system. This significantly improves the interaction between biological tissue and machinery compared to previous models.
Experts believe that bioinformatics is becoming a potential research direction in the context of scientists seeking energy-saving data processing systems with flexible learning capabilities like the human brain.
However, researchers also note that the technology is currently only in experimental scale and needs more years of research to be able to be applied to practical computing systems or artificial intelligence.
